

"""simple docstring"""




import inspect
import unittest

from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch
				from torch import nn

				from transformers import YolosForObjectDetection, YolosModel
				from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import AutoImageProcessor
class      _UpperCAmelCase   :



	def __init__(  self     :       Optional[Any]			,    _lowercase     :       Optional[Any]			,    _lowercase     :       Optional[Any]=13			,    _lowercase     :       Union[str, Any]=[30, 30]			,    _lowercase     :       int=2			,    _lowercase     :       Dict=3			,    _lowercase     :       Optional[int]=True			,    _lowercase     :       Dict=True			,    _lowercase     :       Union[str, Any]=32			,    _lowercase     :       Tuple=5			,    _lowercase     :       Optional[Any]=4			,    _lowercase     :       Any=37			,    _lowercase     :       Optional[int]="gelu"			,    _lowercase     :       Dict=0.1			,    _lowercase     :       List[Any]=0.1			,    _lowercase     :       Dict=10			,    _lowercase     :       Tuple=0.02			,    _lowercase     :       Optional[int]=3			,    _lowercase     :       Union[str, Any]=None			,    _lowercase     :       Optional[int]=8			,    _lowercase     :       Tuple=10			,    ):
		__UpperCAmelCase      		= parent
		__UpperCAmelCase      		= batch_size
		__UpperCAmelCase      		= image_size
		__UpperCAmelCase      		= patch_size
		__UpperCAmelCase      		= num_channels
		__UpperCAmelCase      		= is_training
		__UpperCAmelCase      		= use_labels
		__UpperCAmelCase      		= hidden_size
		__UpperCAmelCase      		= num_hidden_layers
		__UpperCAmelCase      		= num_attention_heads
		__UpperCAmelCase      		= intermediate_size
		__UpperCAmelCase      		= hidden_act
		__UpperCAmelCase      		= hidden_dropout_prob
		__UpperCAmelCase      		= attention_probs_dropout_prob
		__UpperCAmelCase      		= type_sequence_label_size
		__UpperCAmelCase      		= initializer_range
		__UpperCAmelCase      		= num_labels
		__UpperCAmelCase      		= scope
		__UpperCAmelCase      		= n_targets
		__UpperCAmelCase      		= num_detection_tokens
		# we set the expected sequence length (which is used in several tests)
		# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
		__UpperCAmelCase      		= (image_size[1] // patch_size) * (image_size[0] // patch_size)
		__UpperCAmelCase      		= num_patches + 1 + self.num_detection_tokens



	def        a  (  self     :       str						):
		__UpperCAmelCase      		= floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]						)

		__UpperCAmelCase      		= None
		if self.use_labels:
			# labels is a list of Dict (each Dict being the labels for a given example in the batch)
			__UpperCAmelCase      		= []
			for i in range(self.batch_size						):
				__UpperCAmelCase      		= {}
				__UpperCAmelCase      		= torch.randint(
				    high=self.num_labels			,    size=(self.n_targets,)			,    device=_lowercase						)
				__UpperCAmelCase      		= torch.rand(self.n_targets			,    4			,    device=_lowercase						)
				labels.append(_lowercase						)

		__UpperCAmelCase      		= self.get_config()

		return config, pixel_values, labels



	def        a  (  self     :       Any						):
		return YolosConfig(
		    image_size=self.image_size			,    patch_size=self.patch_size			,    num_channels=self.num_channels			,    hidden_size=self.hidden_size			,    num_hidden_layers=self.num_hidden_layers			,    num_attention_heads=self.num_attention_heads			,    intermediate_size=self.intermediate_size			,    hidden_act=self.hidden_act			,    hidden_dropout_prob=self.hidden_dropout_prob			,    attention_probs_dropout_prob=self.attention_probs_dropout_prob			,    is_decoder=_lowercase			,    initializer_range=self.initializer_range			,    num_detection_tokens=self.num_detection_tokens			,    num_labels=self.num_labels			,    )



	def        a  (  self     :       Dict			,    _lowercase     :       Dict			,    _lowercase     :       List[Any]			,    _lowercase     :       List[Any]						):
		__UpperCAmelCase      		= YolosModel(config=_lowercase						)
		model.to(_lowercase						)
		model.eval()
		__UpperCAmelCase      		= model(_lowercase						)
		self.parent.assertEqual(
		    result.last_hidden_state.shape			,    (self.batch_size, self.expected_seq_len, self.hidden_size)						)



	def        a  (  self     :       List[Any]			,    _lowercase     :       Dict			,    _lowercase     :       Optional[int]			,    _lowercase     :       Dict						):
		__UpperCAmelCase      		= YolosForObjectDetection(_lowercase						)
		model.to(_lowercase						)
		model.eval()

		__UpperCAmelCase      		= model(pixel_values=_lowercase						)
		__UpperCAmelCase      		= model(_lowercase						)

		self.parent.assertEqual(result.logits.shape			,    (self.batch_size, self.num_detection_tokens, self.num_labels + 1)						)
		self.parent.assertEqual(result.pred_boxes.shape			,    (self.batch_size, self.num_detection_tokens, 4)						)

		__UpperCAmelCase      		= model(pixel_values=_lowercase			,    labels=_lowercase						)

		self.parent.assertEqual(result.loss.shape			,    ()						)
		self.parent.assertEqual(result.logits.shape			,    (self.batch_size, self.num_detection_tokens, self.num_labels + 1)						)
		self.parent.assertEqual(result.pred_boxes.shape			,    (self.batch_size, self.num_detection_tokens, 4)						)



	def        a  (  self     :       Tuple						):
		__UpperCAmelCase      		= self.prepare_config_and_inputs()
		__UpperCAmelCase		,							__UpperCAmelCase		,							__UpperCAmelCase      		= config_and_inputs
		__UpperCAmelCase      		= {'''pixel_values''': pixel_values}
		return config, inputs_dict






@require_torch
class      _UpperCAmelCase   (							_lowerCAmelCase		,    _lowerCAmelCase		,    unittest.TestCase       ):
	a__		: str					=					(YolosModel, YolosForObjectDetection) if is_torch_available() else ()
	a__		: Optional[Any]					=					(
	    {"feature-extraction": YolosModel, "object-detection": YolosForObjectDetection} if is_torch_available() else {}
	)

	a__		: List[Any]					=					False
	a__		: Any					=					False
	a__		: int					=					False
	a__		: List[Any]					=					False



	def        a  (  self     :       List[str]			,    _lowercase     :       str			,    _lowercase     :       Optional[int]			,    _lowercase     :       str=False						):
		__UpperCAmelCase      		= super()._prepare_for_class(_lowercase			,    _lowercase			,    return_labels=_lowercase						)

		if return_labels:
			if model_class.__name__ == "YolosForObjectDetection":
				__UpperCAmelCase      		= []
				for i in range(self.model_tester.batch_size						):
					__UpperCAmelCase      		= {}
					__UpperCAmelCase      		= torch.ones(
					    size=(self.model_tester.n_targets,)			,    device=_lowercase			,    dtype=torch.long						)
					__UpperCAmelCase      		= torch.ones(
					    self.model_tester.n_targets			,    4			,    device=_lowercase			,    dtype=torch.float						)
					labels.append(_lowercase						)
				__UpperCAmelCase      		= labels

		return inputs_dict



	def        a  (  self     :       Dict						):
		__UpperCAmelCase      		= YolosModelTester(self						)
		__UpperCAmelCase      		= ConfigTester(self			,    config_class=_lowercase			,    has_text_modality=_lowercase			,    hidden_size=37						)



	def        a  (  self     :       List[Any]						):
		self.config_tester.run_common_tests()



	def        a  (  self     :       Optional[int]						):
		# YOLOS does not use inputs_embeds
		pass



	def        a  (  self     :       Any						):
		__UpperCAmelCase		,							__UpperCAmelCase      		= self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			__UpperCAmelCase      		= model_class(_lowercase						)
			self.assertIsInstance(model.get_input_embeddings()			,    (nn.Module)						)
			__UpperCAmelCase      		= model.get_output_embeddings()
			self.assertTrue(x is None or isinstance(_lowercase			,    nn.Linear						)						)



	def        a  (  self     :       Any						):
		__UpperCAmelCase		,							__UpperCAmelCase      		= self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			__UpperCAmelCase      		= model_class(_lowercase						)
			__UpperCAmelCase      		= inspect.signature(model.forward						)
			# signature.parameters is an OrderedDict => so arg_names order is deterministic
			__UpperCAmelCase      		= [*signature.parameters.keys()]

			__UpperCAmelCase      		= ['''pixel_values''']
			self.assertListEqual(arg_names[:1]			,    _lowercase						)



	def        a  (  self     :       str						):
		__UpperCAmelCase      		= self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*_lowercase						)



	def        a  (  self     :       Any						):
		__UpperCAmelCase		,							__UpperCAmelCase      		= self.model_tester.prepare_config_and_inputs_for_common()
		__UpperCAmelCase      		= True

		# in YOLOS, the seq_len is different
		__UpperCAmelCase      		= self.model_tester.expected_seq_len
		for model_class in self.all_model_classes:
			__UpperCAmelCase      		= True
			__UpperCAmelCase      		= False
			__UpperCAmelCase      		= True
			__UpperCAmelCase      		= model_class(_lowercase						)
			model.to(_lowercase						)
			model.eval()
			with torch.no_grad():
				__UpperCAmelCase      		= model(**self._prepare_for_class(_lowercase			,    _lowercase						)						)
			__UpperCAmelCase      		= outputs.attentions
			self.assertEqual(len(_lowercase						)			,    self.model_tester.num_hidden_layers						)

			# check that output_attentions also work using config
			del inputs_dict["output_attentions"]
			__UpperCAmelCase      		= True
			__UpperCAmelCase      		= model_class(_lowercase						)
			model.to(_lowercase						)
			model.eval()
			with torch.no_grad():
				__UpperCAmelCase      		= model(**self._prepare_for_class(_lowercase			,    _lowercase						)						)
			__UpperCAmelCase      		= outputs.attentions
			self.assertEqual(len(_lowercase						)			,    self.model_tester.num_hidden_layers						)

			self.assertListEqual(
			    list(attentions[0].shape[-3:]						)			,    [self.model_tester.num_attention_heads, seq_len, seq_len]			,    )
			__UpperCAmelCase      		= len(_lowercase						)

			# Check attention is always last and order is fine
			__UpperCAmelCase      		= True
			__UpperCAmelCase      		= True
			__UpperCAmelCase      		= model_class(_lowercase						)
			model.to(_lowercase						)
			model.eval()
			with torch.no_grad():
				__UpperCAmelCase      		= model(**self._prepare_for_class(_lowercase			,    _lowercase						)						)

			__UpperCAmelCase      		= 1
			self.assertEqual(out_len + added_hidden_states			,    len(_lowercase						)						)

			__UpperCAmelCase      		= outputs.attentions

			self.assertEqual(len(_lowercase						)			,    self.model_tester.num_hidden_layers						)
			self.assertListEqual(
			    list(self_attentions[0].shape[-3:]						)			,    [self.model_tester.num_attention_heads, seq_len, seq_len]			,    )



	def        a  (  self     :       str						):
		def check_hidden_states_output(_lowercase     :       List[Any]			,    _lowercase     :       Any			,    _lowercase     :       Optional[Any]						):
			__UpperCAmelCase      		= model_class(_lowercase						)
			model.to(_lowercase						)
			model.eval()

			with torch.no_grad():
				__UpperCAmelCase      		= model(**self._prepare_for_class(_lowercase			,    _lowercase						)						)

			__UpperCAmelCase      		= outputs.hidden_states

			__UpperCAmelCase      		= getattr(
			    self.model_tester			,    '''expected_num_hidden_layers'''			,    self.model_tester.num_hidden_layers + 1						)
			self.assertEqual(len(_lowercase						)			,    _lowercase						)

			# YOLOS has a different seq_length
			__UpperCAmelCase      		= self.model_tester.expected_seq_len

			self.assertListEqual(
			    list(hidden_states[0].shape[-2:]						)			,    [seq_length, self.model_tester.hidden_size]			,    )

		__UpperCAmelCase		,							__UpperCAmelCase      		= self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			__UpperCAmelCase      		= True
			check_hidden_states_output(_lowercase			,    _lowercase			,    _lowercase						)

			# check that output_hidden_states also work using config
			del inputs_dict["output_hidden_states"]
			__UpperCAmelCase      		= True

			check_hidden_states_output(_lowercase			,    _lowercase			,    _lowercase						)



	def        a  (  self     :       Dict						):
		__UpperCAmelCase      		= self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_object_detection(*_lowercase						)



	@slow
	def        a  (  self     :       Optional[Any]						):
		for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			__UpperCAmelCase      		= YolosModel.from_pretrained(_lowercase						)
			self.assertIsNotNone(_lowercase						)






def       lowercase__						(       ):
	__UpperCAmelCase      		= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
	return image




@require_torch
@require_vision
class      _UpperCAmelCase   (							unittest.TestCase       ):



	@cached_property
	def        a  (  self     :       str						):
		return AutoImageProcessor.from_pretrained('''hustvl/yolos-small'''						) if is_vision_available() else None



	@slow
	def        a  (  self     :       List[str]						):
		__UpperCAmelCase      		= YolosForObjectDetection.from_pretrained('''hustvl/yolos-small'''						).to(_lowercase						)

		__UpperCAmelCase      		= self.default_image_processor
		__UpperCAmelCase      		= prepare_img()
		__UpperCAmelCase      		= image_processor(images=_lowercase			,    return_tensors='''pt'''						).to(_lowercase						)

		# forward pass
		with torch.no_grad():
			__UpperCAmelCase      		= model(inputs.pixel_values						)

		# verify outputs
		__UpperCAmelCase      		= torch.Size((1, 1_00, 92)						)
		self.assertEqual(outputs.logits.shape			,    _lowercase						)

		__UpperCAmelCase      		= torch.tensor(
		    [[-24.0_248, -10.3_024, -14.8_290], [-42.0_392, -16.8_200, -27.4_334], [-27.2_743, -11.8_154, -18.7_148]]			,    device=_lowercase			,    )
		__UpperCAmelCase      		= torch.tensor(
		    [[0.2_559, 0.5_455, 0.4_706], [0.2_989, 0.7_279, 0.1_875], [0.7_732, 0.4_017, 0.4_462]]			,    device=_lowercase						)
		self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]			,    _lowercase			,    atol=1E-4						)						)
		self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]			,    _lowercase			,    atol=1E-4						)						)

		# verify postprocessing
		__UpperCAmelCase      		= image_processor.post_process_object_detection(
		    _lowercase			,    threshold=0.3			,    target_sizes=[image.size[::-1]]						)[0]
		__UpperCAmelCase      		= torch.tensor([0.9_994, 0.9_790, 0.9_964, 0.9_972, 0.9_861]						).to(_lowercase						)
		__UpperCAmelCase      		= [75, 75, 17, 63, 17]
		__UpperCAmelCase      		= torch.tensor([335.0_609, 79.3_848, 375.4_216, 187.2_495]						).to(_lowercase						)

		self.assertEqual(len(results['''scores''']						)			,    5						)
		self.assertTrue(torch.allclose(results['''scores''']			,    _lowercase			,    atol=1E-4						)						)
		self.assertSequenceEqual(results['''labels'''].tolist()			,    _lowercase						)
		self.assertTrue(torch.allclose(results['''boxes'''][0, :]			,    _lowercase						)						)


"""simple docstring"""


import inspect
import os
import re

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
SCREAMING_SNAKE_CASE			  =  """src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
SCREAMING_SNAKE_CASE			  =  direct_transformers_import(PATH_TO_TRANSFORMERS)

SCREAMING_SNAKE_CASE			  =  transformers.models.auto.configuration_auto.CONFIG_MAPPING

SCREAMING_SNAKE_CASE			  =  {
    # used to compute the property `self.chunk_length`
    """EncodecConfig""": ["""overlap"""],
    # used as `self.bert_model = BertModel(config, ...)`
    """DPRConfig""": True,
    # not used in modeling files, but it's an important information
    """FSMTConfig""": ["""langs"""],
    # used internally in the configuration class file
    """GPTNeoConfig""": ["""attention_types"""],
    # used internally in the configuration class file
    """EsmConfig""": ["""is_folding_model"""],
    # used during training (despite we don't have training script for these models yet)
    """Mask2FormerConfig""": ["""ignore_value"""],
    # `ignore_value` used during training (despite we don't have training script for these models yet)
    # `norm` used in conversion script (despite not using in the modeling file)
    """OneFormerConfig""": ["""ignore_value""", """norm"""],
    # used during preprocessing and collation, see `collating_graphormer.py`
    """GraphormerConfig""": ["""spatial_pos_max"""],
    # used internally in the configuration class file
    """T5Config""": ["""feed_forward_proj"""],
    # used internally in the configuration class file
    # `tokenizer_class` get default value `T5Tokenizer` intentionally
    """MT5Config""": ["""feed_forward_proj""", """tokenizer_class"""],
    """UMT5Config""": ["""feed_forward_proj""", """tokenizer_class"""],
    # used internally in the configuration class file
    """LongT5Config""": ["""feed_forward_proj"""],
    # used internally in the configuration class file
    """SwitchTransformersConfig""": ["""feed_forward_proj"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """BioGptConfig""": ["""layer_norm_eps"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """GLPNConfig""": ["""layer_norm_eps"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """SegformerConfig""": ["""layer_norm_eps"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """CvtConfig""": ["""layer_norm_eps"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """PerceiverConfig""": ["""layer_norm_eps"""],
    # used internally to calculate the feature size
    """InformerConfig""": ["""num_static_real_features""", """num_time_features"""],
    # used internally to calculate the feature size
    """TimeSeriesTransformerConfig""": ["""num_static_real_features""", """num_time_features"""],
    # used internally to calculate the feature size
    """AutoformerConfig""": ["""num_static_real_features""", """num_time_features"""],
    # used internally to calculate `mlp_dim`
    """SamVisionConfig""": ["""mlp_ratio"""],
    # For (head) training, but so far not implemented
    """ClapAudioConfig""": ["""num_classes"""],
    # Not used, but providing useful information to users
    """SpeechT5HifiGanConfig""": ["""sampling_rate"""],
}


# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
    {
        """CLIPSegConfig""": True,
        """DeformableDetrConfig""": True,
        """DetaConfig""": True,
        """DinatConfig""": True,
        """DonutSwinConfig""": True,
        """EfficientFormerConfig""": True,
        """FSMTConfig""": True,
        """JukeboxConfig""": True,
        """LayoutLMv2Config""": True,
        """MaskFormerSwinConfig""": True,
        """MT5Config""": True,
        """NatConfig""": True,
        """OneFormerConfig""": True,
        """PerceiverConfig""": True,
        """RagConfig""": True,
        """SpeechT5Config""": True,
        """SwinConfig""": True,
        """Swin2SRConfig""": True,
        """Swinv2Config""": True,
        """SwitchTransformersConfig""": True,
        """TableTransformerConfig""": True,
        """TapasConfig""": True,
        """TransfoXLConfig""": True,
        """UniSpeechConfig""": True,
        """UniSpeechSatConfig""": True,
        """WavLMConfig""": True,
        """WhisperConfig""": True,
        # TODO: @Arthur (for `alignment_head` and `alignment_layer`)
        """JukeboxPriorConfig""": True,
        # TODO: @Younes (for `is_decoder`)
        """Pix2StructTextConfig""": True,
    }
)



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				Optional[Any]:







       """simple docstring"""
       UpperCamelCase       			=		False
       for attribute in attributes:
              for modeling_source in source_strings:
                     # check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
                     if (
                         F"config.{attribute}" in modeling_source
                         or F"getattr(config, \"{attribute}\"" in modeling_source
                         or F"getattr(self.config, \"{attribute}\"" in modeling_source
                     ):
                            UpperCamelCase       			=		True
                     # Deal with multi-line cases
                     elif (
                         re.search(
                             RF"getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*\"{attribute}\""		,   UpperCAmelCase_		,   )
                         is not None
                     ):
                            UpperCamelCase       			=		True
                     # `SequenceSummary` is called with `SequenceSummary(config)`
                     elif attribute in [
                         "summary_type",
                         "summary_use_proj",
                         "summary_activation",
                         "summary_last_dropout",
                         "summary_proj_to_labels",
                         "summary_first_dropout",
                     ]:
                            if "SequenceSummary" in modeling_source:
                                   UpperCamelCase       			=		True
                     if attribute_used:
                            break
              if attribute_used:
                     break

    # common and important attributes, even if they do not always appear in the modeling files
       UpperCamelCase       			=		[
           "bos_index",
           "eos_index",
           "pad_index",
           "unk_index",
           "mask_index",
           "image_size",
           "use_cache",
           "out_features",
           "out_indices",
       ]
       UpperCamelCase       			=		["encoder_no_repeat_ngram_size"]

       # Special cases to be allowed
       UpperCamelCase       			=		True
       if not attribute_used:
              UpperCamelCase       			=		False
              for attribute in attributes:
                     # Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
                     if attribute in ["is_encoder_decoder"] and default_value is True:
                            UpperCamelCase       			=		True
                     elif attribute in ["tie_word_embeddings"] and default_value is False:
                            UpperCamelCase       			=		True

                     # Allow cases without checking the default value in the configuration class
                     elif attribute in attributes_to_allow + attributes_used_in_generation:
                            UpperCamelCase       			=		True
                     elif attribute.endswith("_token_id"					):
                            UpperCamelCase       			=		True

                     # configuration class specific cases
                     if not case_allowed:
                            UpperCamelCase       			=		SPECIAL_CASES_TO_ALLOW.get(config_class.__name__		,   []					)
                            UpperCamelCase       			=		allowed_cases is True or attribute in allowed_cases

       return attribute_used or case_allowed



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				Optional[Any]:







       """simple docstring"""
       UpperCamelCase       			=		dict(inspect.signature(config_class.__init__					).parameters					)
       UpperCamelCase       			=		[x for x in list(signature.keys()					) if x not in ["self", "kwargs"]]
       UpperCamelCase       			=		[signature[param].default for param in parameter_names]

       # If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
       # as one variant is used, the test should pass
       UpperCamelCase       			=		{}
       if len(config_class.attribute_map					) > 0:
              UpperCamelCase       			=		{v: k for k, v in config_class.attribute_map.items()}

       # Get the path to modeling source files
       UpperCamelCase       			=		inspect.getsourcefile(UpperCAmelCase_					)
       UpperCamelCase       			=		os.path.dirname(UpperCAmelCase_					)
       # Let's check against all frameworks: as long as one framework uses an attribute, we are good.
       UpperCamelCase       			=		[os.path.join(UpperCAmelCase_		,   UpperCAmelCase_					) for fn in os.listdir(UpperCAmelCase_					) if fn.startswith("modeling_"					)]

       # Get the source code strings
       UpperCamelCase       			=		[]
       for path in modeling_paths:
              if os.path.isfile(UpperCAmelCase_					):
                     with open(UpperCAmelCase_					) as fp:
                            modeling_sources.append(fp.read()					)

       UpperCamelCase       			=		[]
       for config_param, default_value in zip(UpperCAmelCase_		,   UpperCAmelCase_					):
              # `attributes` here is all the variant names for `config_param`
              UpperCamelCase       			=		[config_param]
              # some configuration classes have non-empty `attribute_map`, and both names could be used in the
              # corresponding modeling files. As long as one of them appears, it is fine.
              if config_param in reversed_attribute_map:
                     attributes.append(reversed_attribute_map[config_param]					)

              if not check_attribute_being_used(UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					):
                     unused_attributes.append(attributes[0]					)

       return sorted(UpperCAmelCase_					)



def 						lowerCamelCase__      (				)->				List[str]:







       """simple docstring"""
       UpperCamelCase       			=		{}
       for _config_class in list(CONFIG_MAPPING.values()					):
              # Skip deprecated models
              if "models.deprecated" in _config_class.__module__:
                     continue
              # Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
              UpperCamelCase       			=		[
                  cls
                  for name, cls in inspect.getmembers(
                      inspect.getmodule(_config_class					)		,   lambda UpperCAmelCase_					: inspect.isclass(UpperCAmelCase_					)
                      and issubclass(UpperCAmelCase_		,   UpperCAmelCase_					)
                      and inspect.getmodule(UpperCAmelCase_					) == inspect.getmodule(_config_class					)		,   )
              ]
              for config_class in config_classes_in_module:
                     UpperCamelCase       			=		check_config_attributes_being_used(UpperCAmelCase_					)
                     if len(UpperCAmelCase_					) > 0:
                            UpperCamelCase       			=		unused_attributes

       if len(UpperCAmelCase_					) > 0:
              UpperCamelCase       			=		"The following configuration classes contain unused attributes in the corresponding modeling files:\n"
              for name, attributes in configs_with_unused_attributes.items():
                     error += F"{name}: {attributes}\n"

              raise ValueError(UpperCAmelCase_					)


if __name__ == "__main__":
      check_config_attributes()





